In communications networks, there may be a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the communications network is deployed.
For example, one difficulty in delivering high data rates in wireless communications networks is the natural variability of the radio propagation channels. Power control and adaptive modulation and coding are classical methods that adapt the signal quality and data rates to the current radio channel conditions. Such methods can be used to combat channel fading to achieve a constant data rate. Alternatively, the instantaneous data rate can be adapted to the instantaneous channel fading, to send more data when the channel has favorable conditions and less when it is unfavorable. These methods can be applied both over time and over frequency subcarriers. The power and modulation adaptation generally need to be re-determined over the same time/frequency scale as the channel varies. Since substantial channel variations can occur over a few milliseconds and a few hundred kHz, this calls for cumbersome channel estimation and feedback mechanisms.
Access nodes, such as radio base stations, equipped with a large number of antennas can simultaneously schedule multiple wireless devices at the same time/frequency band and communicate using linear processing such as maximum-ratio (MR) and zero-forcing (ZF). This is an approach to handle data traffic does not require a denser network deployment and each access node can control the interference that it causes to its local area. Using many antennas at the access node along with appropriately chosen precoding results in an effective channel between the access node and the wireless device that is substantially independent of the small-scale fading and appears flat over frequency. This property is often referred to as channel hardening. Communications networks having nodes with many antennas are often referred to as massive multi-user multiple-input-multiple-output (MIMO), abbreviated by massive MIMO or MA-MIMO hereafter.
To see the impact of the number of antennas on the channel variations, consider a multiple-input single-output (MISO) system with an access node having M transmit antennas and using MR precoding. Mathematically, the received signal at the wireless device can be expressed asy=√{square root over (P)}hTws+e, where P is the transmitted power, h denotes the M×1 channel vector,
  w  =            h      *                    h            is the M×1 MR precoding vector, s is the unit-energy transmitted symbol, and e is zero-mean additive white Gaussian noise with variance σ2. It can be shown that the average received SNR scales as M. However, the SNR variations around the mean depend heavily on the number of antennas M and decreases as M increases. This can be seen from FIG. 1 where the average received Signal to Noise Ratio (SNR) as well as the maximum and the minimum received SNR for 100000 random channel realizations are plotted versus the number of antennas. In FIG. 1, the average SNR per antenna is set to 0 dB, i.e.,
      P          σ      2        =  1and an independent Rayleigh fading channel where the elements of h are circularly symmetric complex Gaussian random variables with zero mean and unit variance is assumed. For illustration, the instantaneous received SNR for a random channel realization is also plotted. From FIG. 1 follows that the average SNR increases linearly as M increases, and moreover the SNR variations around the mean decrease with M. Similar observations can be made for ZF precoding. This confirms the fact that in communications network deploying nodes with massive number of antennas, the channel after exploiting a good precoder is almost flat and does not vary with time or frequency.
The channel hardening property can be utilized to simplify the power control and selection of modulation and coding, since the same choice can be used for all available frequencies and over a relatively long time period (depending on the user mobility). This is one of the benefits of massive MIMO over conventional radio access technologies.
One key advantage with massive MIMO systems is that wireless devices can be separated spatially. Hence there is a potential to let wireless devices send uplink data in a non-coordinated fashion. This can lower overhead and give advantages in latency. However, a massive MIMO system can only separate the wireless devices if the wireless devices are assigned orthogonal pilot signals. If all wireless devices are assigned unique pilot signals a very large overhead is created. Hence, according to state of the art, pilot signals are reused, i.e., the same pilot signal is assigned to at least two wireless devices. This leads to a collision probability. Using state of the art methods these collisions result in a loss of resources. For example are the data symbols lost and the collision probability of multiple collisions is handled by using a back-off timer giving an increase in latency.
Hence, there is still a need for an improved handling of wireless devices sending uplink data in a non-coordinated fashion.